Background: Radiomics provides a non-invasive approach for predicting lymph node metastasis (LNM) in cervical cancer, but conventional whole-tumor analysis often overlooks intratumoral heterogeneity.Methods: This study aimed to develop and validate an MRI-based habitat radiomics model for preoperative prediction of pelvic LNM in early-stage cervical cancer. Tumor regions were delineated on diffusion-weighted imaging, and intratumoral habitats were generated using unsupervised K-means clustering. Radiomic features were extracted from whole tumors and habitat subregions, combined with clinical variables, and selected using correlation analysis and LASSO regression. Four models—clinical, conventional radiomics, habitat radiomics, and combined—were constructed and evaluated.Results: In internal validation, the combined model achieved the best performance (AUC = 0.895), outperforming the clinical (AUC = 0.799), conventional radiomics (AUC = 0.611), and habitat models (AUC = 0.872). Calibration and decision curve analyses demonstrated good agreement and clinical utility.Conclusions: Integrating habitat-based radiomics with clinical factors significantly improves the preoperative prediction of LNM, providing a robust and clinically applicable tool for individualized management of cervical cancer patients.
背景：影像组学为预测宫颈癌淋巴结转移提供了一种非侵入性方法，但传统的全肿瘤分析常忽略瘤内异质性。方法：本研究旨在开发并验证一种基于磁共振成像的栖息地影像组学模型，用于早期宫颈癌盆腔淋巴结转移的术前预测。在弥散加权成像上勾画肿瘤区域，并采用无监督K均值聚类生成瘤内栖息地。从全肿瘤及栖息地亚区域提取影像组学特征，结合临床变量，通过相关性分析和LASSO回归进行特征筛选。构建并评估了临床模型、传统影像组学模型、栖息地影像组学模型及联合模型四种预测模型。结果：在内部验证中，联合模型表现出最佳性能（曲线下面积=0.895），优于临床模型（曲线下面积=0.799）、传统影像组学模型（曲线下面积=0.611）及栖息地模型（曲线下面积=0.872）。校准曲线和决策曲线分析显示模型具有良好的一致性和临床实用性。结论：基于栖息地的影像组学特征与临床因素相结合，显著提升了淋巴结转移的术前预测能力，为宫颈癌患者的个体化管理提供了可靠且具有临床适用性的工具。
Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics
肿瘤(癌症)患者之家